import simulacao.sumario as sm
import simulacao.random as rd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
sm = sm.Sumario(n_samples=10) 

header = ['Amostras', 'size', 'mean', 'mode', 'median', 'variance', 'std', 'kurtosis', 'skewness', 'Q1', 'Q3']
formating = [None, 'd','.2f','.2f','.2f','.2f','.2f','.2f','.2f','.2f','.2f']

Binomial

n=10
p=0.5

rand = rd.Random()
x = rand.binomial(n=n, p=p, size=100000)
fig = make_subplots(rows=1, cols=2)

pdf = go.Histogram(x=x, histnorm='probability', name='pdf')
cdf = go.Histogram(x=x, histnorm='probability', cumulative_enabled=True, name='cdf')

fig.append_trace(pdf, 1, 1)
fig.append_trace(cdf, 1, 2)
fig.show()

for size = 1000

esp, obs = sm.binomial(n=n, p=p, size=1000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))

fig = go.Figure(data=[go.Table(header=dict(values=header),
                 cells=dict(values=table_t, format = formating))])
fig.show()

for size = 10000

esp, obs = sm.binomial(n=n, p=p, size=10000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))

fig = go.Figure(data=[go.Table(header=dict(values=header),
                 cells=dict(values=table_t, format = formating))])
fig.show()

for size = 100000

esp, obs = sm.binomial(n=n, p=p, size=100000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))

fig = go.Figure(data=[go.Table(header=dict(values=header),
                 cells=dict(values=table_t, format = formating))])
fig.show()

Geometric

p=0.5

rand = rd.Random()
x = rand.geometric(p=p, size=100000)

fig = make_subplots(rows=1, cols=2)

pdf = go.Histogram(x=x, histnorm='probability', name='pdf')
cdf = go.Histogram(x=x, histnorm='probability', cumulative_enabled=True, name='cdf')

fig.append_trace(pdf, 1, 1)
fig.append_trace(cdf, 1, 2)

fig.show()

for size = 1000

esp, obs = sm.geometric(p=p, size=1000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))

fig = go.Figure(data=[go.Table(header=dict(values=header),
                 cells=dict(values=table_t, format = formating))])
fig.show()

for size = 10000

esp, obs = sm.geometric(p=p, size=10000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))

fig = go.Figure(data=[go.Table(header=dict(values=header),
                 cells=dict(values=table_t, format = formating))])
fig.show()

for size = 100000

esp, obs = sm.geometric(p=p, size=100000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))

fig = go.Figure(data=[go.Table(header=dict(values=header),
                 cells=dict(values=table_t, format = formating))])
fig.show()

Triangular

a=0.
b=1.
c=0.5

rand = rd.Random()
x = rand.triangular(lower=a, upper=b, mode=c, size=100000)

fig = make_subplots(rows=1, cols=2)

pdf = go.Histogram(x=x, histnorm='probability', name='pdf')
cdf = go.Histogram(x=x, histnorm='probability', cumulative_enabled=True, name='cdf')

fig.append_trace(pdf, 1, 1)
fig.append_trace(cdf, 1, 2)

fig.show()

for size = 1000

esp, obs = sm.triangular(lower=a, upper=b, mode=c, size=1000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))

fig = go.Figure(data=[go.Table(header=dict(values=header),
                 cells=dict(values=table_t, format = formating))])
fig.show()

for size = 10000

esp, obs = sm.triangular(lower=a, upper=b, mode=c, size=10000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))

fig = go.Figure(data=[go.Table(header=dict(values=header),
                 cells=dict(values=table_t, format = formating))])
fig.show()

for size = 100000

esp, obs = sm.triangular(lower=a, upper=b, mode=c, size=100000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))

fig = go.Figure(data=[go.Table(header=dict(values=header),
                 cells=dict(values=table_t, format = formating))])
fig.show()

Weibull

alpha=1.
beta=1.

rand = rd.Random()
x = rand.weibull(alpha=alpha, beta=beta, size=100000)

fig = make_subplots(rows=1, cols=2)

pdf = go.Histogram(x=x, histnorm='probability', name='pdf')
cdf = go.Histogram(x=x, histnorm='probability', cumulative_enabled=True, name='cdf')

fig.append_trace(pdf, 1, 1)
fig.append_trace(cdf, 1, 2)
fig.show()

for size = 1000

esp, obs = sm.weibull(alpha=alpha, beta=beta, size=1000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))

fig = go.Figure(data=[go.Table(header=dict(values=header),
                 cells=dict(values=table_t, format = formating))])
fig.show()

for size = 10000

esp, obs = sm.weibull(alpha=alpha, beta=beta, size=10000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))

fig = go.Figure(data=[go.Table(header=dict(values=header),
                 cells=dict(values=table_t, format = formating))])
fig.show()

for size = 100000

esp, obs = sm.weibull(alpha=alpha, beta=beta, size=100000)
table = sm.gerar_tabela(esp,obs)
table_t = list(map(list,zip(*table)))

fig = go.Figure(data=[go.Table(header=dict(values=header),
                 cells=dict(values=table_t, format = formating))])
fig.show()